一种可变锚框候选区域网络的目标检测方法

李承昊; 茹乐; 何林远; 迟文升

doi:10.13700/j.bh.1001-5965.2019.0531

一种可变锚框候选区域网络的目标检测方法

doi: 10.13700/j.bh.1001-5965.2019.0531

李承昊¹,
茹乐^2, ,,
何林远¹,
迟文升^{2, 3}

1.
空军工程大学航空工程学院, 西安 710038
2.
空军工程大学装备管理与无人机工程学院, 西安 710038
3.
西安电子科技大学机电工程学院, 西安 710126

基金项目:

国家自然科学基金 61701524

中国博士后科学基金 2019M653742

陕西省自然科学基础研究计划 2017JM6071

详细信息

作者简介:
李承昊  男, 硕士研究生。主要研究方向:计算机视觉、航空电子信息及其智能化

茹乐  男, 博士, 教授, 博士生导师。主要研究方向:抗干扰通信、航空电子信息及其智能化

何林远  男, 博士, 副教授。主要研究方向:图像去雾、超分辨率重建、模式识别

通讯作者:
茹乐. E-mail:ru-le@163.com

中图分类号: V221⁺.3;TB553
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- 被引次数: 0
出版历程
- 收稿日期: 2019-09-29
- 录用日期: 2019-11-01
- 网络出版日期: 2020-08-20

Target detection method for region proposal network with variable anchor box

LI Chenghao¹,
RU Le^{2
, ,},
HE Linyuan¹,
CHI Wensheng^{2, 3}

1.
Aeronautics Engineering Institute, Air Force Engineering University, Xi'an 710038, China
2.
Equipment Management and Unmanned Aerial Vehicle Engineering College, Air Force Engineering University, Xi'an 710038, China
3.
School of Mechano-Electronic Engineering, Xidian University, Xi'an 710126, China

Funds:

National Natural Science Foundation of China 61701524

China Postdoctoral Science Foundation 2019M653742

Natural Science Basic Research Program of Shaanxi 2017JM6071

More Information

Corresponding author: RU Le. E-mail:ru-le@163.com

摘要

摘要:
目标检测作为计算机视觉领域的热点问题，目前基于深度学习的目标检测方法可以分为2类：两步检测和一步检测，前者有着较高准确性，后者有着较好速度，但是为提高检测的性能两者都引入了锚机制。为提高目标检测系统的性能，基于深度卷积神经网络的两步检测算法引入了注意力引导（AG）模块，通过对候选区域网络（RPN）的锚机制进行引导，使得对于预选锚框形状的选择更具有多样性；同时针对传统的后处理方式非极大值抑制（NMS）算法存在的误检和漏检的问题，提出了一种置信度因子的NMS（Cf-NMS）算法，对于模型的整体性能有着很大的贡献。实验结果说明，所提方法虽然在速度性能上有略微的下降，但是无论是在RPN变体还是现有的先进算法在准确性方面都有提升。
- 计算机视觉 /
- 目标检测 /
- 深度学习 /
- 候选区域网络(RPN) /
- 非极大值抑制(NMS)
Abstract:
Object detection is a hot topic in the field of computer vision. At present, object detection methods based on deep learning can be divided into two categories: two-stage detection and one-stage detection. The former has higher accuracy, while the latter has better speed. In order to improve the performance of detection, anchor mechanism is introduced in both categories. In this paper, Attention Guidance (AG) module is introduced in the two-stage detection method based on the deep convolutional neural network, which guides the anchor mechanism of Region Proposal Network (RPN), making the selection of preselected box shape more diversified. At the same time, to solve the problem of false detection and missed detection in the traditional post-processing Non-Maximum Suppression (NMS) algorithm, a Confidence factor NMS (Cf-NMS) method is proposed, which makes a great contribution to the overall performance of the model. Experiment results showed that, although it has a slight decrease in speed performance, the proposed method has an improvement in accuracy in both the RPN variant and the existing advanced method.
- computer vision /
- object detection /
- deep learning /
- Region Proposal Network (RPN) /
- Non-Maximum Suppression (NMS)

HTML全文

图 1 锚点映射

Figure 1. Anchor mapping

下载: 全尺寸图片幻灯片

图 2 网络框架

Figure 2. Network framework

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图 3 注意力引导模块

Figure 3. Attention guidance module

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图 4 检测结果示例

Figure 4. Sample of test results

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表 1 候选区域的平均查全率

Table 1. Average recall rate of region proposal

方法	AR₁₀₀	AR₃₀₀	AR₅₀₀	AR_S	AR_M	AR_L	运行时间/s
RPN+9 anchors	45.7	53.4	57.8	28.7	52.8	63.9	0.09
RPN+12 anchors	50.2	56.6	58.3	33.9	58.2	67.5	0.09
RPN+AG	53.2	60.2	61.3	39.6	62.3	70.2	0.11

下载: 导出CSV

表 2 不同NMS算法的mAP的实验结果

Table 2. Experimental results of mAP by different NMS algorithms

方法	mAP/%
方法	R_IoU=0.3	R_IoU=0.4	R_IoU=0.5	R_IoU=0.6	R_IoU=0.7
贪心NMS	69.6	70	69.1	64.4	56.6
Ccds-NMS^[17]	70.6	70.1	69.5	64.6	56.8
Seg-NMS^[18]	71.5	71.4	68.8	64.6	57.0
Cf-NMS	72.1	71.5	69.4	66.3	60.9

下载: 导出CSV

表 3 检测性能比较

Table 3. Comparison of detection performance

算法	AP	AP₅₀	AP₇₅	AP_S	AP_M	AP_L
FPN^[10]	36.2	59.1	39.0	18.2	39.0	48.2
Mask R-CNN^[9]	39.8	62.3	43.4	22.1	43.2	51.2
Cascade R-CNN^[8]	42.8	62.1	46.3	23.7	45.5	55.2
AG+Cf-NMS+ Faster R-CNN	43.6	62.8	47.4	24.1	46.8	55.3

下载: 导出CSV

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